See Math Libraries. cudaDeviceSynchronize()blocks the calling CPU thread until all CUDA calls previously issued by the thread are completed. If the transfer time exceeds the execution time, a rough estimate for the overall time is tT + tE/nStreams. In this case, multiple broadcasts from different banks are coalesced into a single multicast from the requested shared memory locations to the threads. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Detecting Hardware and Software Configuration. An example would be modeling how two molecules interact with each other, where the molecule sizes are fixed. An exception is the case where all threads in a warp address the same shared memory address, resulting in a broadcast. In the kernel launch, specify the total shared memory needed, as in the following. The host system and the device each have their own distinct attached physical memories 1. Please refer to the EULA for details. It is also the only way for applications to run on devices that did not exist at the time the application was compiled. Optimizations can be applied at various levels, from overlapping data transfers with computation all the way down to fine-tuning floating-point operation sequences. The discussions in this guide all use the C++ programming language, so you should be comfortable reading C++ code. Threads on a CPU are generally heavyweight entities. Throughout this guide, Kepler refers to devices of compute capability 3.x, Maxwell refers to devices of compute capability 5.x, Pascal refers to device of compute capability 6.x, Volta refers to devices of compute capability 7.0, Turing refers to devices of compute capability 7.5, and NVIDIA Ampere GPU Architecture refers to devices of compute capability 8.x. math libraries or deep learning frameworks) do not have a direct dependency on the CUDA runtime, compiler or driver. Incorrect or unexpected results arise principally from issues of floating-point accuracy due to the way floating-point values are computed and stored. This value is expressed in milliseconds and has a resolution of approximately half a microsecond. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Therefore, to get the largest speedup for a fixed problem size, it is worthwhile to spend effort on increasing P, maximizing the amount of code that can be parallelized. Timeline comparison for copy and kernel execution. The NVIDIA Ampere GPU architectures Streaming Multiprocessor (SM) provides the following improvements over Volta and Turing. Kernels can be written using the CUDA instruction set architecture, called PTX, which is described in the PTX reference manual. There are multiple ways to declare shared memory inside a kernel, depending on whether the amount of memory is known at compile time or at run time. However, based on what you've described here, your algorithm might be amenable to an approach similar to what is outlined in the threadfence reduction sample. Zero copy is a feature that was added in version 2.2 of the CUDA Toolkit. Using these data items, the peak theoretical memory bandwidth of the NVIDIA Tesla V100 is 898 GB/s: \(\left. An additional set of Perl and Python bindings are provided for the NVML API. In both cases, kernels must be compiled into binary code by nvcc (called cubins) to execute on the device. Not the answer you're looking for? Using UVA, on the other hand, the physical memory space to which a pointer points can be determined simply by inspecting the value of the pointer using cudaPointerGetAttributes(). Reading from a texture while writing to its underlying global memory array in the same kernel launch should be avoided because the texture caches are read-only and are not invalidated when the associated global memory is modified. As a result, Thrust can be utilized in rapid prototyping of CUDA applications, where programmer productivity matters most, as well as in production, where robustness and absolute performance are crucial. The right value for minBlocksPerMultiprocessor should be determined using a detailed per kernel analysis. Since you don't indicate where your "locally sorted" data resides, this could indicate a copying of that much data at least (for example, if they are locally sorted and reside in shared memory). Weaknesses in customers product designs may affect the quality and reliability of the NVIDIA product and may result in additional or different conditions and/or requirements beyond those contained in this document. There are many possible approaches to profiling the code, but in all cases the objective is the same: to identify the function or functions in which the application is spending most of its execution time. For single-precision code, use of the float type and the single-precision math functions are highly recommended. The performance guidelines and best practices described in the CUDA C++ Programming Guide and the CUDA C++ Best Practices Guide apply to all CUDA-capable GPU architectures. So while the impact is still evident it is not as large as we might have expected. The device will record a timestamp for the event when it reaches that event in the stream. Is it possible to share a Cuda context between applications - Introduction CUDA is a parallel computing platform and programming model created by Nvidia. Both correctable single-bit and detectable double-bit errors are reported. To minimize bank conflicts, it is important to understand how memory addresses map to memory banks. The NVIDIA Nsight Visual Studio Edition for Microsoft Windows 7, Windows HPC Server 2008, Windows 8.1, and Windows 10 is available as a free plugin for Microsoft Visual Studio; see: https://developer.nvidia.com/nsight-visual-studio-edition. // Number of bytes for persisting accesses. Creating additional contexts incurs memory overhead for per-context data and time overhead for context switching. Information published by NVIDIA regarding third-party products or services does not constitute a license from NVIDIA to use such products or services or a warranty or endorsement thereof. Starting with CUDA 11, the toolkit versions are based on an industry-standard semantic versioning scheme: .X.Y.Z, where: .X stands for the major version - APIs have changed and binary compatibility is broken. Not using intermediate registers can help reduce register pressure and can increase kernel occupancy. The CUDA Toolkits End-User License Agreement (EULA) allows for redistribution of many of the CUDA libraries under certain terms and conditions. Always check the error return values on all CUDA API functions, even for functions that are not expected to fail, as this will allow the application to detect and recover from errors as soon as possible should they occur. For applications that need additional functionality or performance beyond what existing parallel libraries or parallelizing compilers can provide, parallel programming languages such as CUDA C++ that integrate seamlessly with existing sequential code are essential. Functions following the __functionName() naming convention map directly to the hardware level. Avoid long sequences of diverged execution by threads within the same warp. OpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc. NVIDIA and the NVIDIA logo are trademarks or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Handling New CUDA Features and Driver APIs, 15.4.1.4. If L1-caching is enabled on these devices, the number of required transactions is equal to the number of required 128-byte aligned segments. //Such that up to 20MB of data is resident. As a result, a read from constant memory costs one memory read from device memory only on a cache miss; otherwise, it just costs one read from the constant cache. A trivial example is when the controlling condition depends only on (threadIdx / WSIZE) where WSIZE is the warp size. and one element in the streaming data section. Hence, its important to design your application to use threads and blocks in a way that maximizes hardware utilization and to limit practices that impede the free distribution of work. Choosing execution parameters is a matter of striking a balance between latency hiding (occupancy) and resource utilization. APOD is a cyclical process: initial speedups can be achieved, tested, and deployed with only minimal initial investment of time, at which point the cycle can begin again by identifying further optimization opportunities, seeing additional speedups, and then deploying the even faster versions of the application into production. Strong Scaling and Amdahls Law, 3.1.3.2. Another, more aggressive, option is -use_fast_math, which coerces every functionName() call to the equivalent __functionName() call. Because the memory copy and the kernel both return control to the host immediately, the host function cpuFunction() overlaps their execution. For global memory accesses, this comparison of requested memory bandwidth to actual memory bandwidth is reported by the Global Memory Load Efficiency and Global Memory Store Efficiency metrics. Like the other calls in this listing, their specific operation, parameters, and return values are described in the CUDA Toolkit Reference Manual. To ensure correct results when parallel threads cooperate, we must synchronize the threads. As even CPU architectures will require exposing parallelism in order to improve or simply maintain the performance of sequential applications, the CUDA family of parallel programming languages (CUDA C++, CUDA Fortran, etc.) - the incident has nothing to do with me; can I use this this way? As an example, the assignment operator in the following sample code has a high throughput, but, crucially, there is a latency of hundreds of clock cycles to read data from global memory: Much of this global memory latency can be hidden by the thread scheduler if there are sufficient independent arithmetic instructions that can be issued while waiting for the global memory access to complete. The warp wide reduction operations support arithmetic add, min, and max operations on 32-bit signed and unsigned integers and bitwise and, or and xor operations on 32-bit unsigned integers. Low Priority: Use zero-copy operations on integrated GPUs for CUDA Toolkit version 2.2 and later. The cudaMemcpyAsync() function is a non-blocking variant of cudaMemcpy() in which control is returned immediately to the host thread. Can anyone please tell me how to do these two operations? We can then launch this kernel onto the GPU and retrieve the results without requiring major rewrites to the rest of our application. For example, to compute the effective bandwidth of a 2048 x 2048 matrix copy, the following formula could be used: \(\text{Effective\ bandwidth} = \left( {\left( 2048^{2} \times 4 \times 2 \right) \div 10^{9}} \right) \div \text{time}\). Shared Memory. A C-style function interface (cuda_runtime_api.h). A Sequential but Misaligned Access Pattern, 9.2.2.2. If sequential threads in a warp access memory that is sequential but not aligned with a 32-byte segment, five 32-byte segments will be requested, as shown in Figure 4. For more details refer to the memcpy_async section in the CUDA C++ Programming Guide. (Note that the CUDA compiler considers any device code that does not contribute to a write to global memory as dead code subject to elimination, so we must at least write something out to global memory as a result of our addressing logic in order to successfully apply this strategy.). The performance on a device of any compute capability can be improved by reading a tile of A into shared memory as shown in Using shared memory to improve the global memory load efficiency in matrix multiplication. Therefore, an application that compiled successfully on an older version of the toolkit may require changes in order to compile against a newer version of the toolkit. For GPUs with compute capability 8.6 maximum shared memory per thread block is 99 KB. For best performance, there should be some coherence in memory access by adjacent threads running on the device. A shared memory request for a warp is not split as with devices of compute capability 1.x, meaning that bank conflicts can occur between threads in the first half of a warp and threads in the second half of the same warp. Bfloat16 provides 8-bit exponent i.e., same range as FP32, 7-bit mantissa and 1 sign-bit. Data that cannot be laid out so as to enable coalescing, or that doesnt have enough locality to use the L1 or texture caches effectively, will tend to see lesser speedups when used in computations on GPUs. The following documents are especially important resources: In particular, the optimization section of this guide assumes that you have already successfully downloaded and installed the CUDA Toolkit (if not, please refer to the relevant CUDA Installation Guide for your platform) and that you have a basic familiarity with the CUDA C++ programming language and environment (if not, please refer to the CUDA C++ Programming Guide). In such cases, users or developers can still benefit from not having to upgrade the entire CUDA Toolkit or driver to use these libraries or frameworks. It allows developers to use a CUDA-enabled graphics processing unit (GPU) to accelerate processing tasks in their applications. This feature enables CUDA kernels to overlap copying data from global to shared memory with computation. As even CPU architectures require exposing this parallelism in order to improve or simply maintain the performance of sequential applications, the CUDA family of parallel programming languages (CUDA C++, CUDA Fortran, etc.) exchange data) between threadblocks, the only method is to use global memory. This chapter discusses the various kinds of memory on the host and device and how best to set up data items to use the memory effectively. These instructions also avoid using extra registers for memory copies and can also bypass the L1 cache. The following example illustrates the basic technique. The C++ host code generated by nvcc utilizes the CUDA Runtime, so applications that link to this code will depend on the CUDA Runtime; similarly, any code that uses the cuBLAS, cuFFT, and other CUDA Toolkit libraries will also depend on the CUDA Runtime, which is used internally by these libraries. For devices of compute capability 8.0 (i.e., A100 GPUs) shared memory capacity per SM is 164 KB, a 71% increase compared to V100s capacity of 96 KB. Note that in Improvement by reading additional data into shared memory, a __syncthreads() call is required after reading the B tile because a warp reads data from shared memory that were written to shared memory by different warps. // (Must be less than cudaDeviceProp::accessPolicyMaxWindowSize), // Hint for L2 cache hit ratio for persisting accesses in the num_bytes region. This is possible because the distribution of the warps across the block is deterministic as mentioned in SIMT Architecture of the CUDA C++ Programming Guide. When using NVRTC, it is recommended that the resulting PTX code is first transformed to the final device code via the steps outlined by the PTX user workflow. Table 2. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Whats the grammar of "For those whose stories they are"? As a result, it is recommended that first-time readers proceed through the guide sequentially. It will not allow any other CUDA call to begin until it has completed.) NVIDIA products are not designed, authorized, or warranted to be suitable for use in medical, military, aircraft, space, or life support equipment, nor in applications where failure or malfunction of the NVIDIA product can reasonably be expected to result in personal injury, death, or property or environmental damage. 32/48/64/96/128K depending on the GPU and current configuration) and each block can use a chunk of it by declaring shared memory. On the other hand, if the data is only accessed once, such data accesses can be considered to be streaming. Shared memory enables cooperation between threads in a block. Best performance with synchronous copy is achieved when the copy_count parameter is a multiple of 4 for all three element sizes. When using branch predication, none of the instructions whose execution depends on the controlling condition is skipped. Dynamic parallelism - passing contents of shared memory to spawned blocks? The current GPU core temperature is reported, along with fan speeds for products with active cooling. Because the data is not cached on the GPU, mapped pinned memory should be read or written only once, and the global loads and stores that read and write the memory should be coalesced. Because kernel1 and kernel2 are executed in different, non-default streams, a capable device can execute the kernels at the same time. The same goes for other CUDA Toolkit libraries: cuFFT has an interface similar to that of FFTW, etc. For example, on a device of compute capability 7.0, a kernel with 128-thread blocks using 37 registers per thread results in an occupancy of 75% with 12 active 128-thread blocks per multi-processor, whereas a kernel with 320-thread blocks using the same 37 registers per thread results in an occupancy of 63% because only four 320-thread blocks can reside on a multiprocessor. (Developers targeting a single machine with known configuration may choose to skip this section.). Any CPU timer can be used to measure the elapsed time of a CUDA call or kernel execution. To analyze performance, it is necessary to consider how warps access global memory in the for loop. Devices of compute capability 3.x allow a third setting of 32KB shared memory / 32KB L1 cache which can be obtained using the optioncudaFuncCachePreferEqual. Since there are many possible optimizations that can be considered, having a good understanding of the needs of the application can help to make the process as smooth as possible. The remainder of the kernel code is identical to the staticReverse() kernel. The peak theoretical bandwidth between the device memory and the GPU is much higher (898 GB/s on the NVIDIA Tesla V100, for example) than the peak theoretical bandwidth between host memory and device memory (16 GB/s on the PCIe x16 Gen3). If B has not finished writing its element before A tries to read it, we have a race condition, which can lead to undefined behavior and incorrect results. As described in Memory Optimizations of this guide, bandwidth can be dramatically affected by the choice of memory in which data is stored, how the data is laid out and the order in which it is accessed, as well as other factors. For example, transferring two matrices to the device to perform a matrix addition and then transferring the results back to the host will not realize much performance benefit. This allows applications that depend on these libraries to redistribute the exact versions of the libraries against which they were built and tested, thereby avoiding any trouble for end users who might have a different version of the CUDA Toolkit (or perhaps none at all) installed on their machines. Shared memory Bank Conflicts: Shared memory bank conflicts exist and are common for the strategy used. Between 128 and 256 threads per block is a good initial range for experimentation with different block sizes. Because separate registers are allocated to all active threads, no swapping of registers or other state need occur when switching among GPU threads. For some architectures L1 and shared memory use same hardware and are configurable. See Version Management for details on how to query the available CUDA software API versions. There are several key strategies for parallelizing sequential code. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality. The size is implicitly determined from the third execution configuration parameter when the kernel is launched. The high-priority recommendations from those guides are as follows: Find ways to parallelize sequential code. To get a closer match between values, set the x86 host processor to use regular double or single precision (64 bits and 32 bits, respectively). In this code, two streams are created and used in the data transfer and kernel executions as specified in the last arguments of the cudaMemcpyAsync call and the kernels execution configuration. Examples include modeling fluids or structures as meshes or grids and some Monte Carlo simulations, where increasing the problem size provides increased accuracy. The application should also maximize parallel execution at a higher level by explicitly exposing concurrent execution on the device through streams, as well as maximizing concurrent execution between the host and the device. Not all threads need to participate. Users wishing to take advantage of such a feature should query its availability with a dynamic check in the code: Alternatively the applications interface might not work at all without a new CUDA driver and then its best to return an error right away: A new error code is added to indicate that the functionality is missing from the driver you are running against: cudaErrorCallRequiresNewerDriver. CUDA 11.0 introduces an async-copy feature that can be used within device code to explicitly manage the asynchronous copying of data from global memory to shared memory. rev2023.3.3.43278. Global memory loads and stores by threads of a warp are coalesced by the device into as few as possible transactions. Fast, low-precision interpolation between texels, Valid only if the texture reference returns floating-point data, Can be used only with normalized texture coordinates, 1 The automatic handling of boundary cases in the bottom row of Table 4 refers to how a texture coordinate is resolved when it falls outside the valid addressing range. Is it known that BQP is not contained within NP? Because shared memory is shared by threads in a thread block, it provides a mechanism for threads to cooperate. NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice. The maximum number of registers per thread is 255. These results are substantially lower than the corresponding measurements for the C = AB kernel. For a listing of some of these tools, see https://developer.nvidia.com/cluster-management. However, it also can act as a constraint on occupancy. High Priority: To maximize developer productivity, profile the application to determine hotspots and bottlenecks. When choosing the first execution configuration parameter-the number of blocks per grid, or grid size - the primary concern is keeping the entire GPU busy. Compatibility of the CUDA platform is thus intended to address a few scenarios: NVIDIA driver upgrades to systems with GPUs running in production for enterprises or datacenters can be complex and may need advance planning. This is important for a number of reasons; for example, it allows the user to profit from their investment as early as possible (the speedup may be partial but is still valuable), and it minimizes risk for the developer and the user by providing an evolutionary rather than revolutionary set of changes to the application. 11.x). A very important performance consideration in programming for CUDA-capable GPU architectures is the coalescing of global memory accesses. A shared memory request for a warp is split into one request for the first half of the warp and one request for the second half of the warp. UVA is also a necessary precondition for enabling peer-to-peer (P2P) transfer of data directly across the PCIe bus or NVLink for supported GPUs in supported configurations, bypassing host memory. To specify an alternate path where the libraries will be distributed, use linker options similar to those below: For Linux and Mac, the -rpath option is used as before. In this calculation, the memory clock rate is converted in to Hz, multiplied by the interface width (divided by 8, to convert bits to bytes) and multiplied by 2 due to the double data rate. Some calculations use 10243 instead of 109 for the final calculation. Performance benefits can be more readily achieved when this ratio is higher. See Compute Capability 5.x in the CUDA C++ Programming Guide for further details. The for loop over i multiplies a row of A by a column of B, which is then written to C. The effective bandwidth of this kernel is 119.9 GB/s on an NVIDIA Tesla V100. Coalescing concepts are illustrated in the following simple examples. When we can, we should use registers. Last updated on Feb 27, 2023. Shared memory enables cooperation between threads in a block. Devices of compute capability 2.0 and higher have the additional ability to multicast shared memory accesses, meaning that multiple accesses to the same location by any number of threads within a warp are served simultaneously. Although the CUDA Runtime provides the option of static linking, some libraries included in the CUDA Toolkit are available only in dynamically-linked form. For example, Overlapping computation and data transfers demonstrates how host computation in the routine cpuFunction() is performed while data is transferred to the device and a kernel using the device is executed. Whether a device has this capability is indicated by the asyncEngineCount field of the cudaDeviceProp structure (or listed in the output of the deviceQuery CUDA Sample). Such a pattern is shown in Figure 3. NVIDIA products are sold subject to the NVIDIA standard terms and conditions of sale supplied at the time of order acknowledgement, unless otherwise agreed in an individual sales agreement signed by authorized representatives of NVIDIA and customer (Terms of Sale). For the NVIDIA Tesla V100, global memory accesses with no offset or with offsets that are multiples of 8 words result in four 32-byte transactions. For exponentiation using base 2 or 10, use the functions exp2() or expf2() and exp10() or expf10() rather than the functions pow() or powf(). Block-column matrix multiplied by block-row matrix. An application that exhibits linear strong scaling has a speedup equal to the number of processors used. (See also the__launch_bounds__ qualifier discussed in Execution Configuration of the CUDA C++ Programming Guide to control the number of registers used on a per-kernel basis.). While compiler optimization improvements continually seek to narrow this gap, explicit multiplication (or the use of an equivalent purpose-built inline function or macro) can have a significant advantage. As mentioned in Occupancy, higher occupancy does not always equate to better performance. Conditionally use features to remain compatible against older drivers. The NVIDIA A100 GPU increases the HBM2 memory capacity from 32 GB in V100 GPU to 40 GB in A100 GPU. Indicate whether the NVIDIA driver stays loaded when no applications are connected to the GPU. To learn more, see our tips on writing great answers. Also of note is the Thrust library, which is a parallel C++ template library similar to the C++ Standard Template Library. Regardless of this possibility, it is good practice to verify that no higher-priority recommendations have been overlooked before undertaking lower-priority items. Computing a row of a tile. To measure performance accurately, it is useful to calculate theoretical and effective bandwidth. The combined L1 cache capacity for GPUs with compute capability 8.6 is 128 KB. (e.g. Reproduction of information in this document is permissible only if approved in advance by NVIDIA in writing, reproduced without alteration and in full compliance with all applicable export laws and regulations, and accompanied by all associated conditions, limitations, and notices. The reciprocal square root should always be invoked explicitly as rsqrtf() for single precision and rsqrt() for double precision. The ideal scenario is one in which many threads perform a substantial amount of work.